A novel analytical method was developed in this study by combining online solid phase extraction with ultra performance liquid chromatography-tandem mass spectrometry(Online SPE-UPLC-MS/MS)for simultaneous determination of 50 kinds of steroid hormones in surface water.Specifically,after high-speed centrifugation of 4 mL water samples,the supernatant was directly injected into an Oasis HLB online SPE column for enrichment and purification.Subsequently,the target compounds were transferred to the analytical column via valve switching for separation and analysis.The chromatographic separation was performed on a Thermo Acclaim RSLC C18 column(100 mm×2.1 mm,2.2 μm),using a mobile phase composed of 5 mmol/L ammonium fluoride aqueous solution and acetonitrile.Mass spectrometric detection was conducted in positive ion mode,utilizing multiple reaction monitoring(MRM)with quantification achieved by the internal standard method.The method validation demonstrated that the limits of detection(LOD)for the 50 kinds of steroid hormones ranged from 0.02 to 0.50 ng/L,while the limits of quantification(LOQ)were between 0.08 and 1.67 ng/L.The average recoveries in surface water samples at spiked concentrations of 5,20 and 200 ng/L were between 74.1％and 119％,with relative standard deviations(RSDs)of 0.2％to 9.9％.This method was applied to analyze 11 surface water samples collected from sites surrounding a pharmaceutical and chemical industrial park.A total of 44 kinds of steroid hormones were detected,with concentrations ranging from 0.11 to 88.6 ng/L,revealing the presence of hormone contamination in the environmental waters surrounding industrial areas.Compared with the traditional offline SPE methods,the proposed online SPE technique significantly reduced sample volume requirements and pretreatment time,while minimizing the loss of target compounds during the pretreatment process.Moreover,compared to reported online SPE techniques,this method achieved high-throughput analysis of multiple classes of steroid hormones,with lower detection limits and higher recoveries.Overall,this method provided rapid sample preparation,high sensitivity,and excellent stability,making it suitable for the direct analysis of trace steroid hormones in surface water.

Time-of-flight secondary ion mass spectrometer(TOF-SIMS)is a highly sensitive surface analysis instrument with high spatial resolution.Traditional TOF-SIMS instruments for sample targets use high field extraction methods.Although the ion collection efficiency is high,it is prone to issues such as low-energy ion beam defocusing,sample morphology sensitivity,and organic molecule ion dissociation.This study aimed to develope an efficient low-field ion extraction system suitable for TOF-SIMS with a continuous beam source.The SIMION simulation software was used to construct a model of the secondary ion optical extraction system.The key factors affecting the extraction efficiency were studied,and the structural parameters of the extraction cone were optimized.Using an indium target as the sample,an experimental test of the performance of the ion extraction system was carried out on the TOF-SIMS instrument.The influences of the voltages of the ion extraction cone and the single lens on the ion extraction efficiency were consistent with the simulation results.By adopting the technology of deflection and coaxial dynamic compensation,the imaging field of view of the ion extraction system was increased to 500 μm×500 μm.The energy window of the ion extraction system reached 10 eV,and the large imaging depth of field of 400 μm was achieved.In the test of a 5 mg/L cholesterol thin film sample,the signal-to-noise ratio of the characteristic peak[M-OH]+reached 4453.The results showed that this low-field secondary ion extraction system effectively improved the performance of the continuous beam TOF-SIMS instrument.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

OBJECTIVE: To assess the efficacy of Qingda Granule (QDG) in ameliorating hypertension-induced cardiac damage and investigate the underlying mechanisms involved. METHODS: Twenty spontaneously hypertensive rats (SHRs) were used to develope a hypertension-induced cardiac damage model. Another 10 Wistar Kyoto (WKY) rats were used as normotension group. Rats were administrated intragastrically QDG [0.9 g/(kg•d)] or an equivalent volume of pure water for 8 weeks. Blood pressure, histopathological changes, cardiac function, levels of oxidative stress and inflammatory response markers were measured. Furthermore, to gain insights into the potential mechanisms underlying the protective effects of QDG against hypertension-induced cardiac injury, a network pharmacology study was conducted. Predicted results were validated by Western blot, radioimmunoassay immunohistochemistry and quantitative polymerase chain reaction, respectively. RESULTS: The administration of QDG resulted in a significant decrease in blood pressure levels in SHRs (P<0.01). Histological examinations, including hematoxylin-eosin staining and Masson trichrome staining revealed that QDG effectively attenuated hypertension-induced cardiac damage. Furthermore, echocardiography demonstrated that QDG improved hypertension-associated cardiac dysfunction. Enzyme-linked immunosorbent assay and colorimetric method indicated that QDG significantly reduced oxidative stress and inflammatory response levels in both myocardial tissue and serum (P<0.01). CONCLUSIONS Both network pharmacology and experimental investigations confirmed that QDG exerted its beneficial effects in decreasing hypertension-induced cardiac damage by regulating the angiotensin converting enzyme (ACE)/angiotensin II (Ang II)/Ang II receptor type 1 axis and ACE/Ang II/Ang II receptor type 2 axis.
Animals ; Drugs, Chinese Herbal/therapeutic use* ; Hypertension/pathology* ; Renin-Angiotensin System/drug effects* ; Rats, Inbred SHR ; Oxidative Stress/drug effects* ; Male ; Rats, Inbred WKY ; Blood Pressure/drug effects* ; Myocardium/pathology* ; Rats ; Inflammation/pathology*

OBJECTIVE: To explore the efficacy and safety of Juan Bi Pill (JBP) in treatment of active rheumatoid arthritis (RA). METHODS: From February 2017 to May 2018, 115 participants from 4 centers were randomly divided into JBP group (57 cases) and placebo group (58 cases) in a 1:1 ratio using a random number table method. Participants received a dose of JBP (4 g, twice a day, orally) combined with methotrexate (MTX, 10 mg per week) or placebo (4 g, twice a day, orally) combined with MTX for 12 weeks. Participants were required with follow-up visits at 24 and 48 weeks, attending 7 assessment visits. Participants were undergo disease activity assessment 7 times (at baseline and 2, 4, 8, 12, 24, 48 weeks) and safety assessments 6 times (at baseline and 4, 8, 12, 24, 48 weeks). The primary endpoint was 28-joint Disease Activity Score (DAS28-ESR and DAS28-CRP). The secondary endpoints included American College of Rheumatology (ACR) criteria for 20% and 50% improvement (ACR20/50), Health Assessment Questionnaire Disability Index (HAQ-DI), clinical disease activity index (CDAI), visual analog scale (VAS), Short Form-36 (SF-36) score, Medial Outcomes Study (MOS) sleep scale score, serum erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), tender joint count, swollen joint count, and morning stiffness. The adverse reactions were observed during the treatment. RESULTS: After 12 weeks of treatment, DAS28-ESR and DAS28-CRP scores in both groups were lower than before treatment (both P<0.01), while the remission rate of DAS28-ESR and DAS28-CRP and low disease activity of JBP group were higher than those in the placebo group (both P<0.01). JBP demonstrated better efficacy on ACR20 and ACR50 compliance rate at 12 and 48 weeks comparing to placebo (all P<0.05). The CDAI and HAQ-DI score, pain VAS and global VAS change of RA patients and physicians, the serum ESR and CRP levels, and the number of tenderness and swelling joints were lower than before treatment at 4, 8, 12, 24, 48 weeks in both groups (P<0.05 or P<0.01), while the reduction of above indices in the JBP group was more obvious than those in the placebo group at 12 weeks (ESR and CRP, both P<0.05) or at 12 and 48 weeks (all P<0.01). There was no difference in adverse reactions between the 2 groups during treatment (P=0.75). CONCLUSION JBP combined with MTX could effectively reduce disease activity in patients with RA in active stage, reduce the symptoms of arthritis, and improve the quality of life, while ensuring safety, reliability, and fewer adverse effects. (Trial Registration: ClinicalTrials.gov, No. NCT02885597).
Humans ; Arthritis, Rheumatoid/drug therapy* ; Methotrexate/adverse effects* ; Female ; Double-Blind Method ; Male ; Middle Aged ; Treatment Outcome ; Drugs, Chinese Herbal/adverse effects* ; Drug Therapy, Combination ; Adult ; Antirheumatic Agents/adverse effects* ; Aged

OBJECTIVE: Varied acupoint selections represent a potential cause of the uncertainty surrounding the efficacy of acupuncture for knee osteoarthritis (OA). Skin temperature, a guiding factor for acupoint selection, may help to address this issue. This study explored thermal sensitization of acupoints used for the treatment of knee OA. METHODS: This cross-sectional case-control study enrolled cases aged 45-75 years with symptomatic knee OA and age- and gender-matched non-knee OA controls in a 1:1 ratio. All participants underwent infrared thermographic imaging. The primary outcome was the relative skin temperature of acupoint (STA), and the secondary outcome was the absolute STA of 11 acupoints. The Z test was used to compare the relative and absolute STAs between the groups. Principal component analysis was used to extract the common factors (CFs, acupoint cluster) in the STAs. A general linear model was used to identify factors affecting the STA in the knee OA cases. For the group comparisons of relative STA, P < 0.0045 (adjusted for 11 acupoints through Bonferroni correction) was considered to indicate statistical significance. For other analyses, P < 0.05 was used as the threshold for statistical significance. RESULTS: The analysis included 308 participants, consisting of 151 cases (mean age: [64.58 ± 6.67] years; male: 25.83%; mean body mass index: [25.70 ± 3.16] kg/m²) and 157 controls (mean age: [63.37 ± 5.96] years; male: 26.11%; mean body mass index: [24.47 ± 2.84] kg/m²). The relative STAs of ST34 (P = 0.0001), EX-LE2 (P < 0.0001), EX-LE5 (P = 0.0006), SP10 (P < 0.0001), BL40 (P = 0.0012) and GB39 (P = 0.0037) were higher in the knee OA group. No difference was found in the STAs of ST35, ST36, SP9, GB33 and GB34. Four CFs were identified for relative STA in both groups. The acupoints within each CF were consistent between the groups. The mean values of the relative STAs across each CF were higher in the knee OA group. In the knee OA cases, no factors were observed to affect the relative STA, while age and gender were found to affect the absolute STA. CONCLUSION Among patients with knee OA, thermal sensitization occurs in the acupoints of the lower extremity, exhibiting localized and regional thermal consistencies. The thermally sensitized acupoints that we identified in this study, ST34, SP10, EX-LE2, EX-LE5, GB39 and BL40, may be good choices for the acupuncture treatment of knee OA. Please cite this article as: Tu JF, Wang XZ, Yan SY, Wang YR, Yang JW, Shi GX, Zhang WZ, Jing LN, Yang LS, Liu DH, Wang LQ, Mi BH. Thermal sensitization of acupoints in patients with knee osteoarthritis: A cross-sectional case-control study. J Integr Med. 2025; 23(3): 289-296.
Humans ; Osteoarthritis, Knee/physiopathology* ; Male ; Cross-Sectional Studies ; Middle Aged ; Female ; Acupuncture Points ; Case-Control Studies ; Aged ; Skin Temperature ; Acupuncture Therapy

BACKGROUND: A growing body of research is exploring the role of antioxidant and anti-inflammatory dietary supplements in the treatment of osteoarthritis, highlighting an increasing emphasis on non-pharmacological interventions. Although more patients are turning to supplements to manage osteoarthritis, their actual effectiveness remains uncertain. OBJECTIVE: This study aims to provide a comprehensive evaluation of the available evidence concerning the efficacy of various dietary supplements in osteoarthritis treatment. SEARCH STRATEGY: We searched PubMed, Embase, Cochrane Library and Web of Science for studies on the use of various dietary supplements in the treatment of osteoarthritis from the creation of each database until Jan 20, 2025. INCLUSION CRITERIA: (1) Research object: osteoarthritis. (2) Intervention measures: patients in the treatment group received dietary supplements, while the control group received placebos. (3) Research type: randomized controlled trials (RCTs). DATA EXTRACTION AND ANALYSIS: Two researchers independently examined the literature and retrieved data based on predefined criteria. The information gathered included the first author, year of publication, sample size, participant demographics, length of the follow-up period, intervention and control measures, and inclusion indications. RCTs comparing dietary supplements to placebo with the pain and function subscales of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) among patients with osteoarthritis were included. The optimal dietary supplement was identified based on the total ranking by summing the surface under the cumulative ranking curve (SUCRA) of these two scores. Furthermore, the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) was used to confirm the quality of the evidence. RESULTS: Overall, 23 studies covering 21 dietary supplements and involving 2455 participants met the inclusion criteria. In the WOMAC pain score, the SUCRA of passion fruit peel extract was 91% (mean difference [MD]: -9.2; 95% confidence interval [CI]: [-16.0, -2.3]), followed by methylsulfonylmethane (89%), undenatured type II collagen (87%), collagen (84%), and Lanconone (82%). The SUCRA (99%) of passion fruit peel extract (MD: -41.0; 95% CI: [-66.0, -16.0]) ranked first in terms of the WOMAC function score, followed by Lanconone (95%), collagen (86%), ParActin (84%), and Lactobacillus casei strain Shirota (83%). The top three total rankings are passion fruit peel extract (95.0%), Lanconone (88.5%), and collagen (85.0%). However, the GRADE revealed low evidence quality. CONCLUSION Passion fruit peel extract was the best supplement for improving WOMAC pain and function scores in patients with osteoarthritis, followed by Lanconone and collagen. However, further large-scale, well designed RCTs are required to substantiate these promising findings. Please cite this article as: Chen CS, Wen L, Yang F, Deng YC, Ji JH, Chen RJ, Chen Z, Chen G, Gu JY. Effects of dietary supplements on patients with osteoarthritis: A systematic review and network meta-analysis. J Integr Med. 2025; 23(4): 357-369.
Humans ; Dietary Supplements ; Osteoarthritis/drug therapy* ; Randomized Controlled Trials as Topic

OBJECTIVE: To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis (TB) in the Guangdong Province between 2010 and 2019. METHOD: Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering. Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive (ST-CAR) model. RESULTS: Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000 in 2019. Spatial hotspots were found in northeastern Guangdong, particularly in Heyuan, Shanwei, and Shantou, while Shenzhen, Dongguan, and Foshan had the lowest rates in the Pearl River Delta. The ST-CAR model showed that the TB risk was lower with higher per capita Gross Domestic Product (GDP) [Relative Risk ( RR), 0.91; 95% Confidence Interval ( CI): 0.86-0.98], more the ratio of licensed physicians and physician ( RR, 0.94; 95% CI: 0.90-0.98), and higher per capita public expenditure ( RR, 0.94; 95% CI: 0.90-0.97), with a marginal effect of population density ( RR, 0.86; 95% CI: 0.86-1.00). CONCLUSION The incidence of TB in Guangdong varies spatially and temporally. Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection. Strategies focusing on equitable health resource distribution and economic development are the key to TB control.
Humans ; China/epidemiology* ; Incidence ; Bayes Theorem ; Spatio-Temporal Analysis ; Tuberculosis/epidemiology* ; Socioeconomic Factors